EVOLUTION-MANAGER

Edit File: softplus_op.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_SOFTPLUS_OP_H_
#define TENSORFLOW_CORE_KERNELS_SOFTPLUS_OP_H_
// Functor definition for SoftplusOp and SoftplusGradOp, must be compilable by
// nvcc.

// clang-format off
#include "tensorflow/core/platform/bfloat16.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
// clang-format on
#include "tensorflow/core/framework/tensor_types.h"

namespace tensorflow {
namespace functor {

// Functor used by SoftplusOp to do the computations.
template <typename Device, typename T>
struct Softplus {
  // Computes Softplus activation.
  //
  // features: any shape.
  // activations: same shape as "features".
  void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor activations) {
    // Choose a threshold on x below which exp(x) may underflow
    // when added to 1, but for which exp(x) is always within epsilon of the
    // true softplus(x).  Offset of 2 from machine epsilon checked
    // experimentally for float16, float32, float64.  Checked against
    // softplus implemented with numpy's log1p and numpy's logaddexp.
    static const T threshold =
        Eigen::numext::log(Eigen::NumTraits<T>::epsilon()) + T(2);
    // Value above which exp(x) may overflow, but softplus(x) == x
    // is within machine epsilon.
    auto too_large = features > features.constant(-threshold);
    // Value below which exp(x) may underflow, but softplus(x) == exp(x)
    // is within machine epsilon.
    auto too_small = features < features.constant(threshold);
    auto features_exp = features.exp();
    activations.device(d) = too_large.select(
        features,                       // softplus(x) ~= x for x large
        too_small.select(features_exp,  // softplus(x) ~= exp(x) for x small
                         features_exp.log1p()));
  }
};

// Functor used by SoftplusGradOp to do the computations.
template <typename Device, typename T>
struct SoftplusGrad {
  // Computes SoftplusGrad backprops.
  //
  // gradients: gradients backpropagated to the Softplus op.
  // features: inputs that where passed to the Softplus op.
  // backprops: gradients to backpropagate to the Softplus inputs.
  void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
                  typename TTypes<T>::ConstTensor features,
                  typename TTypes<T>::Tensor backprops) {
    backprops.device(d) =
        gradients / ((-features).exp() + features.constant(T(1)));
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_SOFTPLUS_OP_H_